Overview

Dataset statistics

Number of variables15
Number of observations456548
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.2 MiB
Average record size in memory120.0 B

Variable types

Numeric10
Categorical5

Alerts

checkout_price is highly overall correlated with base_price and 1 other fieldsHigh correlation
base_price is highly overall correlated with checkout_price and 2 other fieldsHigh correlation
category is highly overall correlated with base_price and 1 other fieldsHigh correlation
cuisine is highly overall correlated with checkout_price and 2 other fieldsHigh correlation
emailer_for_promotion is highly imbalanced (59.4%)Imbalance
homepage_featured is highly imbalanced (50.3%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-08-10 14:48:30.303484
Analysis finished2023-08-10 14:49:46.071023
Duration1 minute and 15.77 seconds
Software versionydata-profiling vv4.5.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct456548
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1250096.3
Minimum1000000
Maximum1499999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:46.319074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1025009.3
Q11124998.8
median1250183.5
Q31375140.2
95-th percentile1475045.6
Maximum1499999
Range499999
Interquartile range (IQR)250141.5

Descriptive statistics

Standard deviation144354.82
Coefficient of variation (CV)0.11547496
Kurtosis-1.2002881
Mean1250096.3
Median Absolute Deviation (MAD)125071.5
Skewness-0.0011022118
Sum5.7072897 × 1011
Variance2.0838315 × 1010
MonotonicityNot monotonic
2023-08-10T21:49:46.650985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1379560 1
 
< 0.1%
1279808 1
 
< 0.1%
1142819 1
 
< 0.1%
1032545 1
 
< 0.1%
1177082 1
 
< 0.1%
1162112 1
 
< 0.1%
1380689 1
 
< 0.1%
1347391 1
 
< 0.1%
1124739 1
 
< 0.1%
1480565 1
 
< 0.1%
Other values (456538) 456538
> 99.9%
ValueCountFrequency (%)
1000000 1
< 0.1%
1000001 1
< 0.1%
1000002 1
< 0.1%
1000003 1
< 0.1%
1000004 1
< 0.1%
1000005 1
< 0.1%
1000006 1
< 0.1%
1000007 1
< 0.1%
1000008 1
< 0.1%
1000009 1
< 0.1%
ValueCountFrequency (%)
1499999 1
< 0.1%
1499998 1
< 0.1%
1499997 1
< 0.1%
1499995 1
< 0.1%
1499994 1
< 0.1%
1499993 1
< 0.1%
1499992 1
< 0.1%
1499991 1
< 0.1%
1499990 1
< 0.1%
1499989 1
< 0.1%

week
Real number (ℝ)

Distinct145
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.768771
Minimum1
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:46.957082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q139
median76
Q3111
95-th percentile139
Maximum145
Range144
Interquartile range (IQR)72

Descriptive statistics

Standard deviation41.524956
Coefficient of variation (CV)0.55537834
Kurtosis-1.1795031
Mean74.768771
Median Absolute Deviation (MAD)36
Skewness-0.049517056
Sum34135533
Variance1724.322
MonotonicityNot monotonic
2023-08-10T21:49:47.346573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 3359
 
0.7%
105 3348
 
0.7%
106 3347
 
0.7%
140 3332
 
0.7%
123 3331
 
0.7%
134 3330
 
0.7%
133 3324
 
0.7%
113 3312
 
0.7%
100 3309
 
0.7%
143 3305
 
0.7%
Other values (135) 423251
92.7%
ValueCountFrequency (%)
1 2922
0.6%
2 2896
0.6%
3 2899
0.6%
4 2889
0.6%
5 2859
0.6%
6 2846
0.6%
7 2795
0.6%
8 2786
0.6%
9 2854
0.6%
10 2859
0.6%
ValueCountFrequency (%)
145 3268
0.7%
144 3302
0.7%
143 3305
0.7%
142 3238
0.7%
141 3263
0.7%
140 3332
0.7%
139 3279
0.7%
138 3278
0.7%
137 3283
0.7%
136 3273
0.7%

center_id
Real number (ℝ)

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.105796
Minimum10
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:47.662343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q143
median76
Q3110
95-th percentile161
Maximum186
Range176
Interquartile range (IQR)67

Descriptive statistics

Standard deviation45.975046
Coefficient of variation (CV)0.55994885
Kurtosis-0.80521955
Mean82.105796
Median Absolute Deviation (MAD)33
Skewness0.34513021
Sum37485237
Variance2113.7049
MonotonicityNot monotonic
2023-08-10T21:49:47.941952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 7046
 
1.5%
10 7015
 
1.5%
52 6993
 
1.5%
43 6970
 
1.5%
67 6915
 
1.5%
137 6894
 
1.5%
174 6891
 
1.5%
51 6880
 
1.5%
27 6853
 
1.5%
108 6843
 
1.5%
Other values (67) 387248
84.8%
ValueCountFrequency (%)
10 7015
1.5%
11 6801
1.5%
13 7046
1.5%
14 6041
1.3%
17 6333
1.4%
20 6671
1.5%
23 6434
1.4%
24 5233
1.1%
26 5085
1.1%
27 6853
1.5%
ValueCountFrequency (%)
186 5528
1.2%
177 5296
1.2%
174 6891
1.5%
162 4366
1.0%
161 5591
1.2%
157 5721
1.3%
153 6696
1.5%
152 5920
1.3%
149 5021
1.1%
146 6164
1.4%

meal_id
Real number (ℝ)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2024.3375
Minimum1062
Maximum2956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:48.245766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1062
5-th percentile1198
Q11558
median1993
Q32539
95-th percentile2760
Maximum2956
Range1894
Interquartile range (IQR)981

Descriptive statistics

Standard deviation547.42092
Coefficient of variation (CV)0.27041979
Kurtosis-1.2431114
Mean2024.3375
Median Absolute Deviation (MAD)499
Skewness-0.17288413
Sum9.2420722 × 108
Variance299669.66
MonotonicityNot monotonic
2023-08-10T21:49:48.578396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2290 11138
 
2.4%
1062 11137
 
2.4%
1727 11136
 
2.4%
1109 11134
 
2.4%
1754 11132
 
2.4%
2707 11123
 
2.4%
1778 11121
 
2.4%
1993 11116
 
2.4%
1962 11114
 
2.4%
1971 11102
 
2.4%
Other values (41) 345295
75.6%
ValueCountFrequency (%)
1062 11137
2.4%
1109 11134
2.4%
1198 4206
 
0.9%
1207 10806
2.4%
1216 9695
2.1%
1230 10746
2.4%
1247 7184
1.6%
1248 9939
2.2%
1311 4682
1.0%
1438 4385
 
1.0%
ValueCountFrequency (%)
2956 3319
 
0.7%
2867 8092
1.8%
2826 11057
2.4%
2760 10209
2.2%
2707 11123
2.4%
2704 9811
2.1%
2664 9853
2.2%
2640 10747
2.4%
2631 10458
2.3%
2581 11072
2.4%

checkout_price
Real number (ℝ)

HIGH CORRELATION 

Distinct1992
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean332.23893
Minimum2.97
Maximum866.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:48.903355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.97
5-th percentile121.28
Q1228.95
median296.82
Q3445.23
95-th percentile640.23
Maximum866.27
Range863.3
Interquartile range (IQR)216.28

Descriptive statistics

Standard deviation152.93972
Coefficient of variation (CV)0.46033053
Kurtosis-0.25281949
Mean332.23893
Median Absolute Deviation (MAD)104.73
Skewness0.6723299
Sum1.5168302 × 108
Variance23390.559
MonotonicityNot monotonic
2023-08-10T21:49:49.191008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
290.03 7342
 
1.6%
291.03 7276
 
1.6%
292.03 7200
 
1.6%
486.03 6635
 
1.5%
484.03 6631
 
1.5%
485.03 6584
 
1.4%
280.33 5833
 
1.3%
281.33 5828
 
1.3%
282.33 5729
 
1.3%
447.23 5240
 
1.1%
Other values (1982) 392250
85.9%
ValueCountFrequency (%)
2.97 1
 
< 0.1%
45.62 1
 
< 0.1%
47.59 1
 
< 0.1%
53.41 1
 
< 0.1%
55.35 3
< 0.1%
56.26 1
 
< 0.1%
58.26 1
 
< 0.1%
64.02 3
< 0.1%
65.02 5
< 0.1%
65.96 1
 
< 0.1%
ValueCountFrequency (%)
866.27 1
 
< 0.1%
767.33 217
< 0.1%
766.33 222
< 0.1%
765.33 184
< 0.1%
760.54 2
 
< 0.1%
759.57 1
 
< 0.1%
759.54 1
 
< 0.1%
758.54 1
 
< 0.1%
757.63 2
 
< 0.1%
756.63 2
 
< 0.1%

base_price
Real number (ℝ)

HIGH CORRELATION 

Distinct1907
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.15663
Minimum55.35
Maximum866.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:49.477276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum55.35
5-th percentile144.53
Q1243.5
median310.46
Q3458.87
95-th percentile668.33
Maximum866.27
Range810.92
Interquartile range (IQR)215.37

Descriptive statistics

Standard deviation160.71591
Coefficient of variation (CV)0.45379897
Kurtosis-0.50616167
Mean354.15663
Median Absolute Deviation (MAD)111.49
Skewness0.63766087
Sum1.616895 × 108
Variance25829.605
MonotonicityNot monotonic
2023-08-10T21:49:49.871497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292.03 9513
 
2.1%
290.03 9384
 
2.1%
291.03 9379
 
2.1%
280.33 6838
 
1.5%
282.33 6561
 
1.4%
281.33 6477
 
1.4%
445.23 6310
 
1.4%
446.23 6265
 
1.4%
447.23 6235
 
1.4%
486.03 5607
 
1.2%
Other values (1897) 383979
84.1%
ValueCountFrequency (%)
55.35 1
< 0.1%
64.02 1
< 0.1%
65.02 1
< 0.1%
66.02 1
< 0.1%
72.75 1
< 0.1%
73.75 1
< 0.1%
74.75 1
< 0.1%
75.66 1
< 0.1%
79.54 1
< 0.1%
81.54 2
< 0.1%
ValueCountFrequency (%)
866.27 3
 
< 0.1%
865.27 4
 
< 0.1%
864.27 1
 
< 0.1%
767.33 284
0.1%
766.33 282
0.1%
765.33 292
0.1%
760.54 1
 
< 0.1%
759.57 1
 
< 0.1%
759.54 1
 
< 0.1%
758.54 2
 
< 0.1%

emailer_for_promotion
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
0
419498 
1
 
37050

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters456548
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 419498
91.9%
1 37050
 
8.1%

Length

2023-08-10T21:49:50.164965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T21:49:50.393879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 419498
91.9%
1 37050
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 419498
91.9%
1 37050
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 456548
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 419498
91.9%
1 37050
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 456548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 419498
91.9%
1 37050
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 419498
91.9%
1 37050
 
8.1%

homepage_featured
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
0
406693 
1
49855 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters456548
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 406693
89.1%
1 49855
 
10.9%

Length

2023-08-10T21:49:50.592093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T21:49:50.808553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 406693
89.1%
1 49855
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 406693
89.1%
1 49855
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 456548
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 406693
89.1%
1 49855
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 456548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 406693
89.1%
1 49855
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 406693
89.1%
1 49855
 
10.9%

num_orders
Real number (ℝ)

Distinct1250
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.87276
Minimum13
Maximum24299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:51.035251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14
Q154
median136
Q3324
95-th percentile878
Maximum24299
Range24286
Interquartile range (IQR)270

Descriptive statistics

Standard deviation395.9228
Coefficient of variation (CV)1.51189
Kurtosis121.85222
Mean261.87276
Median Absolute Deviation (MAD)96
Skewness6.9299661
Sum1.1955748 × 108
Variance156754.86
MonotonicityNot monotonic
2023-08-10T21:49:51.315124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 12397
 
2.7%
15 12294
 
2.7%
14 12270
 
2.7%
28 11548
 
2.5%
27 11462
 
2.5%
26 11457
 
2.5%
41 10344
 
2.3%
40 10179
 
2.2%
42 10083
 
2.2%
54 8845
 
1.9%
Other values (1240) 345669
75.7%
ValueCountFrequency (%)
13 12397
2.7%
14 12270
2.7%
15 12294
2.7%
26 11457
2.5%
27 11462
2.5%
28 11548
2.5%
40 10179
2.2%
41 10344
2.3%
42 10083
2.2%
53 8715
1.9%
ValueCountFrequency (%)
24299 1
< 0.1%
15336 1
< 0.1%
14229 1
< 0.1%
13580 1
< 0.1%
13150 1
< 0.1%
12489 1
< 0.1%
12327 1
< 0.1%
12177 1
< 0.1%
12137 1
< 0.1%
11380 1
< 0.1%

city_code
Real number (ℝ)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean601.5534
Minimum456
Maximum713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:51.607646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum456
5-th percentile478
Q1553
median596
Q3651
95-th percentile700
Maximum713
Range257
Interquartile range (IQR)98

Descriptive statistics

Standard deviation66.195914
Coefficient of variation (CV)0.11004163
Kurtosis-0.79068524
Mean601.5534
Median Absolute Deviation (MAD)53
Skewness-0.20913133
Sum2.74638 × 108
Variance4381.8991
MonotonicityNot monotonic
2023-08-10T21:49:51.890974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
590 54746
 
12.0%
526 43525
 
9.5%
638 20047
 
4.4%
522 13459
 
2.9%
517 13109
 
2.9%
604 13062
 
2.9%
699 12098
 
2.6%
647 11833
 
2.6%
576 11456
 
2.5%
614 11332
 
2.5%
Other values (41) 251881
55.2%
ValueCountFrequency (%)
456 6716
 
1.5%
461 5763
 
1.3%
473 5855
 
1.3%
478 5021
 
1.1%
485 5712
 
1.3%
515 5085
 
1.1%
517 13109
 
2.9%
522 13459
 
2.9%
526 43525
9.5%
541 4501
 
1.0%
ValueCountFrequency (%)
713 6853
1.5%
703 6706
1.5%
702 5264
1.2%
700 6891
1.5%
699 12098
2.6%
698 6434
1.4%
695 5296
1.2%
693 4627
 
1.0%
685 6993
1.5%
683 5296
1.2%

region_code
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.614566
Minimum23
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:52.141175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile34
Q134
median56
Q377
95-th percentile85
Maximum93
Range70
Interquartile range (IQR)43

Descriptive statistics

Standard deviation17.641306
Coefficient of variation (CV)0.31160365
Kurtosis-1.0509314
Mean56.614566
Median Absolute Deviation (MAD)21
Skewness0.056253809
Sum25847267
Variance311.21566
MonotonicityNot monotonic
2023-08-10T21:49:52.337176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
56 191228
41.9%
34 116713
25.6%
77 94612
20.7%
85 30283
 
6.6%
23 6434
 
1.4%
71 6293
 
1.4%
93 5721
 
1.3%
35 5264
 
1.2%
ValueCountFrequency (%)
23 6434
 
1.4%
34 116713
25.6%
35 5264
 
1.2%
56 191228
41.9%
71 6293
 
1.4%
77 94612
20.7%
85 30283
 
6.6%
93 5721
 
1.3%
ValueCountFrequency (%)
93 5721
 
1.3%
85 30283
 
6.6%
77 94612
20.7%
71 6293
 
1.4%
56 191228
41.9%
35 5264
 
1.2%
34 116713
25.6%
23 6434
 
1.4%

center_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
TYPE_A
262881 
TYPE_C
99593 
TYPE_B
94074 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2739288
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTYPE_C
2nd rowTYPE_C
3rd rowTYPE_C
4th rowTYPE_C
5th rowTYPE_C

Common Values

ValueCountFrequency (%)
TYPE_A 262881
57.6%
TYPE_C 99593
 
21.8%
TYPE_B 94074
 
20.6%

Length

2023-08-10T21:49:52.568080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T21:49:52.810946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
type_a 262881
57.6%
type_c 99593
 
21.8%
type_b 94074
 
20.6%

Most occurring characters

ValueCountFrequency (%)
T 456548
16.7%
Y 456548
16.7%
P 456548
16.7%
E 456548
16.7%
_ 456548
16.7%
A 262881
9.6%
C 99593
 
3.6%
B 94074
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2282740
83.3%
Connector Punctuation 456548
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 456548
20.0%
Y 456548
20.0%
P 456548
20.0%
E 456548
20.0%
A 262881
11.5%
C 99593
 
4.4%
B 94074
 
4.1%
Connector Punctuation
ValueCountFrequency (%)
_ 456548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2282740
83.3%
Common 456548
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 456548
20.0%
Y 456548
20.0%
P 456548
20.0%
E 456548
20.0%
A 262881
11.5%
C 99593
 
4.4%
B 94074
 
4.1%
Common
ValueCountFrequency (%)
_ 456548
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2739288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 456548
16.7%
Y 456548
16.7%
P 456548
16.7%
E 456548
16.7%
_ 456548
16.7%
A 262881
9.6%
C 99593
 
3.6%
B 94074
 
3.4%

op_area
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0835899
Minimum0.9
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-08-10T21:49:53.020029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.7
Q13.6
median4
Q34.5
95-th percentile6.7
Maximum7
Range6.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.0916864
Coefficient of variation (CV)0.26733498
Kurtosis1.4510434
Mean4.0835899
Median Absolute Deviation (MAD)0.5
Skewness0.66441361
Sum1864354.8
Variance1.1917792
MonotonicityNot monotonic
2023-08-10T21:49:53.248015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4 52548
 
11.5%
3.9 48533
 
10.6%
3.8 40080
 
8.8%
4.4 26060
 
5.7%
4.5 25688
 
5.6%
2.8 25503
 
5.6%
4.1 23346
 
5.1%
7 20686
 
4.5%
4.8 18648
 
4.1%
3.4 17258
 
3.8%
Other values (20) 158198
34.7%
ValueCountFrequency (%)
0.9 3432
 
0.8%
1.9 4083
 
0.9%
2 9512
 
2.1%
2.4 5021
 
1.1%
2.7 12430
2.7%
2.8 25503
5.6%
2.9 4712
 
1.0%
3 11184
2.4%
3.2 6333
 
1.4%
3.4 17258
3.8%
ValueCountFrequency (%)
7 20686
4.5%
6.7 7046
 
1.5%
6.3 7015
 
1.5%
5.6 6993
 
1.5%
5.3 6053
 
1.3%
5.1 13366
2.9%
5 6164
 
1.4%
4.8 18648
4.1%
4.7 5975
 
1.3%
4.6 5983
 
1.3%

category
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Beverages
127890 
Rice Bowl
33408 
Sandwich
33291 
Pizza
33138 
Starters
29941 
Other values (9)
198880 

Length

Max length12
Median length9
Mean length7.5311577
Min length4

Characters and Unicode

Total characters3438335
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBeverages
2nd rowBeverages
3rd rowBeverages
4th rowBeverages
5th rowBeverages

Common Values

ValueCountFrequency (%)
Beverages 127890
28.0%
Rice Bowl 33408
 
7.3%
Sandwich 33291
 
7.3%
Pizza 33138
 
7.3%
Starters 29941
 
6.6%
Other Snacks 29379
 
6.4%
Desert 29294
 
6.4%
Salad 28559
 
6.3%
Pasta 27694
 
6.1%
Seafood 26916
 
5.9%
Other values (4) 57038
12.5%

Length

2023-08-10T21:49:53.512824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beverages 127890
24.6%
rice 33408
 
6.4%
bowl 33408
 
6.4%
sandwich 33291
 
6.4%
pizza 33138
 
6.4%
starters 29941
 
5.8%
other 29379
 
5.7%
snacks 29379
 
5.7%
desert 29294
 
5.6%
salad 28559
 
5.5%
Other values (6) 111648
21.5%

Most occurring characters

ValueCountFrequency (%)
e 561902
16.3%
a 427237
12.4%
r 280621
 
8.2%
s 267947
 
7.8%
B 181912
 
5.3%
S 160761
 
4.7%
t 159811
 
4.6%
i 151252
 
4.4%
v 127890
 
3.7%
g 127890
 
3.7%
Other values (21) 991112
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2856213
83.1%
Uppercase Letter 519335
 
15.1%
Space Separator 62787
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 561902
19.7%
a 427237
15.0%
r 280621
9.8%
s 267947
9.4%
t 159811
 
5.6%
i 151252
 
5.3%
v 127890
 
4.5%
g 127890
 
4.5%
o 99915
 
3.5%
c 96078
 
3.4%
Other values (12) 555670
19.5%
Uppercase Letter
ValueCountFrequency (%)
B 181912
35.0%
S 160761
31.0%
P 60832
 
11.7%
R 33408
 
6.4%
O 29379
 
5.7%
D 29294
 
5.6%
E 13562
 
2.6%
F 10187
 
2.0%
Space Separator
ValueCountFrequency (%)
62787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3375548
98.2%
Common 62787
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 561902
16.6%
a 427237
12.7%
r 280621
 
8.3%
s 267947
 
7.9%
B 181912
 
5.4%
S 160761
 
4.8%
t 159811
 
4.7%
i 151252
 
4.5%
v 127890
 
3.8%
g 127890
 
3.8%
Other values (20) 928325
27.5%
Common
ValueCountFrequency (%)
62787
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3438335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 561902
16.3%
a 427237
12.4%
r 280621
 
8.2%
s 267947
 
7.8%
B 181912
 
5.3%
S 160761
 
4.7%
t 159811
 
4.6%
i 151252
 
4.4%
v 127890
 
3.7%
g 127890
 
3.7%
Other values (21) 991112
28.8%

cuisine
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Italian
122925 
Thai
118216 
Indian
112612 
Continental
102795 

Length

Max length11
Median length7
Mean length6.8771652
Min length4

Characters and Unicode

Total characters3139756
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThai
2nd rowThai
3rd rowThai
4th rowThai
5th rowThai

Common Values

ValueCountFrequency (%)
Italian 122925
26.9%
Thai 118216
25.9%
Indian 112612
24.7%
Continental 102795
22.5%

Length

2023-08-10T21:49:53.759215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T21:49:54.017196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
italian 122925
26.9%
thai 118216
25.9%
indian 112612
24.7%
continental 102795
22.5%

Most occurring characters

ValueCountFrequency (%)
n 656534
20.9%
a 579473
18.5%
i 456548
14.5%
t 328515
10.5%
I 235537
 
7.5%
l 225720
 
7.2%
T 118216
 
3.8%
h 118216
 
3.8%
d 112612
 
3.6%
C 102795
 
3.3%
Other values (2) 205590
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2683208
85.5%
Uppercase Letter 456548
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 656534
24.5%
a 579473
21.6%
i 456548
17.0%
t 328515
12.2%
l 225720
 
8.4%
h 118216
 
4.4%
d 112612
 
4.2%
o 102795
 
3.8%
e 102795
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
I 235537
51.6%
T 118216
25.9%
C 102795
22.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 3139756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 656534
20.9%
a 579473
18.5%
i 456548
14.5%
t 328515
10.5%
I 235537
 
7.5%
l 225720
 
7.2%
T 118216
 
3.8%
h 118216
 
3.8%
d 112612
 
3.6%
C 102795
 
3.3%
Other values (2) 205590
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3139756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 656534
20.9%
a 579473
18.5%
i 456548
14.5%
t 328515
10.5%
I 235537
 
7.5%
l 225720
 
7.2%
T 118216
 
3.8%
h 118216
 
3.8%
d 112612
 
3.6%
C 102795
 
3.3%
Other values (2) 205590
 
6.5%

Interactions

2023-08-10T21:49:39.244784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:48:58.202743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:02.491117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:07.913693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:13.684458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:19.322012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:24.116901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:28.388018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:32.097062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:35.773978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:39.569342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:48:58.602412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:02.936046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:08.496136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:14.262053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:19.770268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:24.532978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:28.735265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:32.449005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:36.118902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:39.903613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:48:58.962838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:03.329890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:09.185999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:15.094401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:20.186642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:24.908073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:29.091884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:32.843238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:36.472071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:40.228876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:48:59.312354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:03.784215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:09.685030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:15.620339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:20.997055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:25.306182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:29.429035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:33.212024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:36.822349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:40.583789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:48:59.720985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:04.185940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:10.307163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:16.197247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:21.338112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:25.749711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:29.802810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:33.554864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:37.167269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:40.897505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:00.124535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:04.851290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:10.807090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:16.749894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:21.778288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:26.214109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:30.151386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:33.870563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:37.534860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:41.245007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:00.612999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:05.371138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:11.410063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:17.238028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:22.335010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:26.697083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:30.563216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:34.227124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:37.896180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:41.576030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:01.177443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:05.860734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:12.008968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:17.788999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:22.834714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:27.201229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:30.966593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:34.540475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:38.232010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:41.953894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:01.678070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:06.471324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:12.540635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:18.382826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:23.217041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:27.649105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:31.336118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:34.898412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:38.569082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:42.287516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:02.128031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:07.296817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:13.088053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:18.865858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:23.692523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:28.033198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:31.741138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:35.372804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:49:38.917585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-10T21:49:54.452958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idweekcenter_idmeal_idcheckout_pricebase_pricenum_orderscity_coderegion_codeop_areaemailer_for_promotionhomepage_featuredcenter_typecategorycuisine
id1.0000.0020.0020.0000.0020.003-0.002-0.001-0.001-0.0000.0050.0040.0000.0000.002
week0.0021.000-0.0040.0190.0360.037-0.0090.0010.0050.0010.0300.0460.0070.0510.025
center_id0.002-0.0041.0000.0100.002-0.000-0.0550.0670.000-0.0870.0210.0310.2740.0410.032
meal_id0.0000.0190.0101.000-0.021-0.0370.030-0.002-0.0020.0010.1010.1260.0230.4880.399
checkout_price0.0020.0360.002-0.0211.0000.957-0.388-0.006-0.0010.0230.2000.1500.0270.4780.505
base_price0.0030.037-0.000-0.0370.9571.000-0.344-0.0030.0040.0190.2130.1260.0260.5390.599
num_orders-0.002-0.009-0.0550.030-0.388-0.3441.0000.0260.0290.1760.1640.1390.0060.0470.029
city_code-0.0010.0010.067-0.002-0.006-0.0030.0261.0000.0640.0730.0160.0250.3570.0360.025
region_code-0.0010.0050.000-0.002-0.0010.0040.0290.0641.0000.0350.0420.0250.1560.1010.063
op_area-0.0000.001-0.0870.0010.0230.0190.1760.0730.0351.0000.0190.0550.4220.0430.027
emailer_for_promotion0.0050.0300.0210.1010.2000.2130.1640.0160.0420.0191.0000.3910.0090.2620.183
homepage_featured0.0040.0460.0310.1260.1500.1260.1390.0250.0250.0550.3911.0000.0310.1640.098
center_type0.0000.0070.2740.0230.0270.0260.0060.3570.1560.4220.0090.0311.0000.0600.020
category0.0000.0510.0410.4880.4780.5390.0470.0360.1010.0430.2620.1640.0601.0000.848
cuisine0.0020.0250.0320.3990.5050.5990.0290.0250.0630.0270.1830.0980.0200.8481.000

Missing values

2023-08-10T21:49:42.929040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-10T21:49:44.103210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idweekcenter_idmeal_idcheckout_pricebase_priceemailer_for_promotionhomepage_featurednum_orderscity_coderegion_codecenter_typeop_areacategorycuisine
013795601551885136.83152.290017764756TYPE_C2.0BeveragesThai
110187042551885135.83152.290032364756TYPE_C2.0BeveragesThai
211962733551885132.92133.92009664756TYPE_C2.0BeveragesThai
311165274551885135.86134.860016364756TYPE_C2.0BeveragesThai
413438725551885146.50147.500021564756TYPE_C2.0BeveragesThai
514936126551885146.53146.530028564756TYPE_C2.0BeveragesThai
611108327551885145.53146.530014864756TYPE_C2.0BeveragesThai
714611678551885146.53145.530013564756TYPE_C2.0BeveragesThai
811023649551885134.83134.830017564756TYPE_C2.0BeveragesThai
9101813010551885144.56143.560017564756TYPE_C2.0BeveragesThai
idweekcenter_idmeal_idcheckout_pricebase_priceemailer_for_promotionhomepage_featurednum_orderscity_coderegion_codecenter_typeop_areacategorycuisine
4565381259284136612104571.33573.33001547377TYPE_A4.5FishContinental
4565391166111137612104631.53631.53004147377TYPE_A4.5FishContinental
4565401241284138612104631.53630.530115047377TYPE_A4.5FishContinental
4565411314947139612104490.82629.530131247377TYPE_A4.5FishContinental
4565421020517140612104485.03629.530114947377TYPE_A4.5FishContinental
4565431437396141612104583.03630.53011347377TYPE_A4.5FishContinental
4565441060716142612104581.03582.03004247377TYPE_A4.5FishContinental
4565451029231143612104583.03581.03004047377TYPE_A4.5FishContinental
4565461141147144612104582.03581.03005347377TYPE_A4.5FishContinental
4565471443704145612104581.03582.03002747377TYPE_A4.5FishContinental